In our proposed RHGA algorithm, the reinforcement learning helps to learn an adaptive Q-value by Q-learning algorithm during the Q-LKH local search procedure. Actually, both of the adaptive Q-value in Q-LKH and the \(\alpha \) -value in LKH are the replacement metrics to the distance in LK for determining the candidate cities. As the results in ([1]} [1]}, [3]} [3]}), the Q-value and \(\alpha \) -value outperform the distance in determining the candidate cities or evaluating the quality of the edges. So why not replace the distance metric used in the sub-tours merging process in EAX-GA (i.e., Step 5 in section REF )? Therefore, we replace the distance metric used in EAX-GA with the adaptive Q-value in our proposed RHGA algorithm.
